Abstract
In this paper, a new metaheuristic, Electric Charged Particles Optimization (ECPO) algorithm, is developed. This algorithm is inspired by the interaction (forces exerted) between electric charged particles. It this algorithm not all the particles interact with each other, only selected ones. Then the way they interact with each other is defined by the selected strategy among the three available strategies. Therefore, there are several combinations possible between the number of interacting particles and strategies to find the most suitable one for the problem in hand which will help the algorithm to solve a wide range of optimization problems. The performance of the developed algorithm is first tested on the set of problems used for single objective real parameter algorithm competition that was held in the congress on evolutionary computation 2014. Then, the ECPO has been applied to optimal design of circular antenna array for sidelobe level reduction. The obtained results are then compared with other well-known metaheuristics using statistical tools. The analysis of the experimental results shows that the ECPO is a very competitive optimization algorithm.









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Abedinpourshotorban H, Mariyam Shamsuddin S, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22. https://doi.org/10.1016/j.swevo.2015.07.002
Birbil Şİ, Fang S-C (2003) An electromagnetism-like mechanism for global optimization. J Glob Optim 25:263–282. https://doi.org/10.1023/A:1022452626305
Bouchekara HREH (2017) Most valuable player algorithm: a novel optimization algorithm inspired from sport. Oper Res 20:1–57
Bouchekara HREH (2019) Electrostatic discharge algorithm (ESDA): a novel nature-inspired optimization algorithm and its application to worst-case tolerance analysis of an EMC filter. IET Sci Meas Technol. https://doi.org/10.1049/iet-smt.2018.5194IET
Bouchekara HREH, Orlandi A, Al-Qdah M, De Paulis F (2018) Most valuable player algorithm for circular antenna arrays optimization to maximum sidelobe levels reduction. IEEE Trans Electromagn Compat 60:1655–1661. https://doi.org/10.1109/TEMC.2018.2800774
Brownlee J (2011) Clever algorithms: nature-inspired programming recipes. lulu.com, 436 pp
Chen J, Cai H, Wang W (2018) A new metaheuristic algorithm: car tracking optimization algorithm. Soft Comput 22:3857–3878. https://doi.org/10.1007/s00500-017-2845-7
Davarynejad M (2013) Deploying metaheuristics for global optimization. Ferdowsi University of Mashhad, Mashhad
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014
Dib N (2017) Design of planar concentric circular antenna arrays with reduced side lobe level using symbiotic organisms search. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2971-2
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium On Micro Machine And Human Science, pp 39–43
Geem LGV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68. https://doi.org/10.1177/003754970107600201
Hajipour H, Khormuji HB, Rostami H (2016) ODMA: a novel swarm-evolutionary metaheuristic optimizer inspired by open source development model and communities. Soft Comput 20:727–747. https://doi.org/10.1007/s00500-014-1536-x
Haupt RL, Haupt SE (2004) Practical genetic algorithms, 2nd edn. Wiley, Hoboken, NJ, 272 pp
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Michigan
Igual J, Poblet JM, Sarasa JP (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715–734. https://doi.org/10.1007/s00500-018-3102-4
Jaddi NS, Alvankarian J, Abdullah S (2017) Kidney-inspired algorithm for optimization problems. Commun Nonlinear Sci Numer Simul 42:358–369. https://doi.org/10.1016/j.cnsns.2016.06.006
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech Rep TR06, Erciyes Univ 10. doi: citeulike-article-id:6592152
Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85. https://doi.org/10.1016/j.compstruc.2016.01.008
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289. https://doi.org/10.1007/s00707-009-0270-4
Kaveh A, Motie Share MA, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224:85–107. https://doi.org/10.1007/s00707-012-0745-6
Kennedy J, Eberhart R (1995) Particle swarm optimization. Neural Netw 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kirkpatrick S, Vecchi MP (1983) Optimization by simmulated annealing. Science (80-) 220:671–680. https://doi.org/10.1007/BF01009452
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization
Miguel L, Nikolaos R (2013) Derivative-free optimization: a review of algorithms and comparison of software implementations. J Glob Optim 56:1247–1293. https://doi.org/10.1007/s10898-012-9951-y
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022
Mirjalili S, Gandomi AH, Zahra S, Saremi S (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Punnathanam V, Kotecha P (2016) Yin-Yang-pair optimization: a novel lightweight optimization algorithm. Eng Appl Artif Intell 54:62–79. https://doi.org/10.1016/j.engappai.2016.04.004
Robinson J, Rahmat-Samii Y (2004) Particle swarm optimization in electromagnetics. IEEE Trans Antennas Propag 52:397–407. https://doi.org/10.1109/TAP.2004.823969
Simon D, Member S (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328
Tayarani-N MH, Akbarzadeh-T MR (2014) Magnetic-inspired optimization algorithms: operators and structures. Swarm Evol Comput 19:82–101. https://doi.org/10.1016/j.swevo.2014.06.004
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82. https://doi.org/10.1109/4235.585893
Xing B, Gao W-J (2014) Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, Switzerland
Zaldívar D, Morales B, Rodríguez A et al (2018) A novel bio-inspired optimization model based on Yellow Saddle Goatfish behavior. BioSystems 174:1–21. https://doi.org/10.1016/j.biosystems.2018.09.007
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Bouchekara, H.R.E.H. Electric Charged Particles Optimization and its application to the optimal design of a circular antenna array. Artif Intell Rev 54, 1767–1802 (2021). https://doi.org/10.1007/s10462-020-09890-x
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DOI: https://doi.org/10.1007/s10462-020-09890-x